106 results about "Function computation" patented technology

The invention relates to the technical field of numerical control programming, in particular to a five-axis machining path planning method and system based on data driving, and the method comprises the following steps: obtaining real-time coordinates of each axis of a machine tool, calculating linear velocity and angular velocity components to construct a Jacobian matrix, executing singular value decomposition, and calculating a conditional number ratio by using maximum and minimum singular values; and inputting a nonlinear mapping function to calculate a dynamic penalty factor, generating a rotating shaft weighted item in combination with a rotating shaft identifier, constructing a weighted damping least square objective function, calculating a five-axis motion increment, and accumulating the five-axis motion increment with a real-time coordinate to generate a target absolute position coordinate. According to the method, the pose singularity degree is quantified by monitoring the machine tool pose condition number ratio and converted into the dynamic penalty factor to apply the self-adaptive constraint to the rotating shaft, the severe sudden change of the rotating shaft in the singularity area is inhibited, the tool nose track following error is minimized, and meanwhile smooth distribution of the motion increment is achieved; and the dynamic stability and the surface quality of five-axis linkage machining are improved.

The invention belongs to the technical field of data visualization, and particularly relates to a dynamic visualization generation method, system and device based on user behaviors and context awareness and a medium, and the method comprises the steps: generating a structured operation sequence after standardizing original input data; performing time sequence modeling on the operation sequence to generate a user behavior semantic vector, and generating a joint feature vector by adopting an attention mechanism; pre-loading an input data set, and generating a candidate view set in combination with the user intention preference in the joint feature vector; calculating view scores through a layout optimization function, and screening an optimal view scheme with the highest score; dynamically simplifying or enhancing the chart component according to the equipment performance and context parameters in the joint feature vector; calling a rendering engine to output a view, and monitoring a user interaction event; and updating the strategy network through a PPO algorithm, and optimizing a subsequent view generation path. The problem of unsmooth interaction caused by a static template is avoided, and the user experience is remarkably improved.

The invention discloses a program fault processing method, device and equipment of a software system and a storage medium, relates to the technical field of computers, can be applied to financial science and technology and medical health business scenarios, and comprises the steps that a fault knowledge graph of the software system is acquired, the fault knowledge graph is dynamically constructed based on multi-source heterogeneous data, and the fault knowledge graph is stored in a database; wherein a diversified atlas relationship is stored; performing graph neural network driven association analysis based on the system component dependency relationship and the first association relationship, mining a hidden fault propagation path on a component dependency link, and determining a fault association component and a fault root cause corresponding to the program fault data based on the hidden fault propagation path; based on the fault association component, the fault root cause and the second association relationship, determining a plurality of candidate repair schemes corresponding to the program fault data; and calculating evaluation scores of the plurality of candidate repair schemes through the utility function, sorting according to the evaluation scores, and determining an optimal repair scheme of the software system about the program fault in the plurality of candidate repair schemes.

The embodiment of the invention provides a nonlinear function calculation method and a special function calculation unit (SFU). The method comprises the following steps: determining a to-be-fitted function and a fitting interval corresponding to the to-be-fitted function; based on the to-be-fitted function and the fitting interval, configuring segment parameters and linear parameters corresponding to the to-be-fitted function; and performing fitting calculation based on the piecewise parameters and the linear parameters to obtain a first target function corresponding to the to-be-fitted function, and completing nonlinear function calculation based on the first target function. According to the invention, the problems of low precision, poor performance and low efficiency of SFU processing in the prior art are solved, and the effect of improving the SFU processing precision, efficiency and performance is achieved.

The invention discloses an intelligent stereoscopic warehouse inventory data synchronization method and device and a computer program product, and the method comprises the steps: collecting multi-source data of an intelligent stereoscopic warehouse, and constructing a multivariate data set; embedding the multivariate data set into a nonlinear mapping space, and constructing an embedded state vector for expressing inventory behaviors; a scheduling weight evaluation function is constructed based on the embedded state vector and historical error feedback, the scheduling weight of the inventory operation is calculated through the scheduling weight evaluation function, a decision is made on the synchronization mode of the inventory operation according to the scheduling weight, and the synchronization mode comprises instant synchronization, delay synchronization or merging synchronization; and based on the scheduling weight and historical error feedback, dynamically correcting the response tension coefficient of each dimension in the embedded state vector, and applying the corrected response tension coefficient to subsequent embedded state vector construction to form a feedback closed loop. According to the invention, a powerful guarantee is provided for efficient, accurate and stable operation of the intelligent warehousing system.

The invention discloses a robot generative adversarial self-imitation learning method based on large language model feedback, and belongs to the technical field of computer systems based on specific calculation models, and the method comprises the steps: (1) initializing a strategy network, a discriminator network and a demonstration pool; (2) constructing and training a large language model feedback network: obtaining a training sample of the large language model feedback network, and training the large language model feedback network according to the training sample; and (3) an iterative learning step (33): updating the strategy network according to the reward calculated by the new cost function. According to the robot generative adversarial self-imitation learning method based on large language model feedback, the advantages of the large language model in the aspects of semantic comprehension, reasoning and knowledge organization are used as a potential award priori source, and a stable award signal can be provided when the demonstration data quality is poor, so that a strategy exceeding original demonstration is learned.

The invention discloses a hardware-based SOFTMAX function calculation method and device. The hardware-based SOFTMAX function calculation method comprises the following steps: receiving a multi-dimensional input vector corresponding to an SOFTMAX function through an input port; uniformly dividing the multi-dimensional input vector into a plurality of data segments according to an element sequence through a channel distributor, and distributing the data segments to corresponding operation channels; performing an exponential operation on the data segment using the operation channel to generate a local exponential vector; aggregating the local exponent vectors with an accumulator to generate a global exponent sum; distributing the global index sum to each operation channel, so that the operation channels generate output sub-vectors according to the local index vectors and the global index sum; and splicing the output sub-vectors according to the element sequence through a recombination unit to generate an SOFTMAX output vector. The efficient SOFTMAX function hardware acceleration calculation under the multi-channel parallel pipeline processing architecture is realized, and the calculation performance and throughput are improved by several times compared with the traditional serial software implementation.

A method, computer program product, and computer system of artificial intelligence (AI) intervention to detect and mitigate an abnormality associated with an object moving during performance of a process. A trained recurrent neural network (RNN) determines, from sensor data, a probability (Pr1) that the abnormality existed at a first time, where Pr1 exceeds a threshold T1 and in response, an alternative generative adversarial network (AGAN) determines a probability (Pa1) that the abnormality existed at the first time. A score S1, which is computed as a function of Pr1 and Pa1, exceeds T1 and in response the RNN and the AGAN determines, from the senso data, a probability (Pr2) and a probability (Pa2), respectively, that the abnormality existed at a second time. A score S2, which is computed as a function of at least Pr2, Pa2, and (S1-T1), exceeds a threshold (T2) and in response, the abnormality is mitigated.

The invention relates to the technical field of artificial intelligence and privacy protection, and discloses a vertical field large model privacy forgetting method and terminal based on double Fisher matrix guidance, and the method comprises the steps: separating two text sequence data subsets from an original training corpus of a vertical field large language model, namely a reserved data set and a forgetting data set; the reserved data set contains vertical field professional data which does not contain privacy information, and the forgotten data set contains privacy sensitive data to be eliminated. Based on an information geometry theory, calculating a reserved Fisher information matrix and a forgotten Fisher information matrix of model parameters on the reserved data set and the forgotten data set respectively; based on collaborative analysis of a reserved Fisher information matrix and a forgotten Fisher information matrix, a parameter forgotten guide function is constructed, guide function values of all parameters of a model are calculated, and a standardized guide vector is constructed according to the guide function values. According to the method, refined balance of privacy forgetting of the large language model in two levels of privacy elimination and capability keeping is realized.

The application discloses a kind of based on the difference of communication guarantee system and method of computing power service level, the system includes: computing power level construction module, priority generation module, resource scheduling module and closed loop correction module;The computing power level construction module is used to the computing power service object in the algorithm network fusion scene, constructs computing power service level vector and is disassembled into multidimensional computing power service characteristic factor, after normalization processing and weight distribution, the normalized computing power service level comprehensive vector is obtained, provide basis for subsequent computing power level and communication priority mapping, the present application relates to communication guarantee technical field.A kind of based on the difference of communication guarantee system and method of computing power service level, based on the real-time computing power uncertainty value of computing power entropy function calculation system, and combining computing power entropy gradient change rate, communication guarantee priority can be adjusted in time when computing power resource fluctuation is larger, to avoid the communication interruption or delay caused by resource fluctuation.

This disclosure provides a log data processing method, apparatus, system, device, medium, and program product, relating to the field of computer technology, and particularly to cloud computing and function computing technologies. The specific implementation scheme is as follows: the method is applied to a machine, where multiple instances run. The method includes: acquiring metadata, wherein the metadata includes resource scaling parameters and a code package, the resource scaling parameters being used to measure the timing of scaling up or down, and the code package including log data processing rules; running the code package through any target instance among the multiple instances, wherein the machine has a runtime deployed, the runtime providing a framework for running the log data processing program, and the target instance running within the framework provided by the runtime; monitoring the runtime's operating metrics, and scaling up or down the machine based on the operating metrics and resource scaling parameters. This disclosure enables flexible scaling up or down by monitoring the runtime's operating metrics.

Techniques for performing secure computing of softmax functions at high speed and with high accuracy are provided. A secure softmax function calculation system that calculates a share ([[softmax (u1)]], . . . , [[softmax (uJ)]]) from a share ([[u1]], . . . , [[uJ]]) includes a subtraction means for calculating a share ([[u1−u1]], [[u2−u1]], . . . , [[uJ−uJ]]), a first secure batch mapping calculation means for calculating, [[exp (u1−u1)]], [[exp (u2−u1)]], . . . , [[exp (uJ−uJ)]], an addition means for calculating a share([[∑ j=1J⁢exp⁡(uj-u1)]],… ,[[∑ j=1J⁢exp⁡(uj-uJ)]],and a second secure batch mapping calculation means for calculating a share ([[softmax (u1)], . . . , [[softmax (uJ)]]).

The present application relates to a kind of adaptive arctangent function computing system and method based on coprocessor, using coprocessor to complete data transmission and state control.The present application includes data write module, iteration register, iteration angle selection module, data iteration module and data write back module;Data write module reads input data from coprocessor interface and stores in iteration register;Iteration angle selection module selects optimal iteration angle according to the data characteristics in iteration register and sends to data iteration module;Data iteration module calculates iteration data according to the optimal iteration angle and iterates the state in iteration register;Data write back module sends the calculation completion signal to coprocessor and executes the calculation result write back through coprocessor interface.The present application can utilize coprocessor without changing the internal structure of processor, and also can utilize the internal logic calculation unit of processor to realize high-speed high-precision arctangent function calculation.

This invention discloses a dynamic vibration reduction control method. The method involves acquiring parameter combination data of the vibration reduction equipment and inputting it into computational fluid dynamics software for calculation, outputting combined data of the results. Using the combined data of the results as independent variables and the expected loss as the dependent variable, a deep neural network is used for training to obtain a vibration loss function. An optimization algorithm is used to obtain parameter combination data with the minimum expected loss, and then an optimal vibration reduction control strategy function is constructed. The difference between the expected loss and the minimum expected loss is calculated to obtain the expected benefit. The work required to overcome external forces when the appendage switches from normal operating conditions to optimal control conditions is obtained to obtain the control cost function. An exhaustive search or swarm intelligence method is used to obtain the combination that maximizes the comprehensive evaluation function. The optimal comprehensive control strategy function under different flow velocities is obtained. The optimal comprehensive control strategy function is deployed to the controller of the vibration reduction equipment, and the relative position of the appendage is dynamically adjusted according to the measured different inflow velocities.

The invention discloses a method, a system and equipment for realizing full-link gray scale based on function calculation, and a medium. The method comprises the following steps: converting user traffic into an event through a function calculation trigger corresponding to a traffic source of the user traffic from different end sides; generating a request of a target type protocol according to a feature rule list corresponding to a traffic source and the event; and the request is forwarded to the micro-service gateway, so that the full-link gray level capability is realized. Due to the fact that flow of different protocol types can be converted into events of a uniform format by using function calculation triggers of flow sources of different end sides, and requests of a target type protocol can be obtained by combining corresponding feature rule lists, full-link gray scale can be introduced into flow scenes of different protocol types, and the flow scenes of different protocol types can be obtained. And the universality of the full-link gray scale scheme is improved.

The present application relates to the field of machine learning, and discloses a dialogue generation method, comprising: training a pre-constructed text generation model by using historical data, calculating a first loss value between a predicted reply text sequence output by the text generation model during training and a historical real reply sequence by a first loss function, calculating a second loss value between a predicted conversion rate output by the text generation model during training and a real conversion rate by a second loss function, adjusting parameters of the pre-constructed text generation model according to the first loss value and the second loss value, and obtaining a pre-trained text generation model; inputting target dialogue text, identity information of a questioning user in the target dialogue text, and identity information of a replying user into the pre-trained text generation model, and obtaining a target reply text sequence of the replying user to the target dialogue text. The present application also proposes a dialogue generation device, an electronic device and a medium. The present application can accurately generate personalized dialogue content based on natural language processing technology.

The invention relates to the field of big data stream processing, and provides a data stream conflict resolution method and device and a computer storage medium. The method comprises the following steps: receiving a data flow item; calculating a main Hash bucket index and a standby Hash bucket index of the data flow item through the first Hash function and the second Hash function to locate a main Hash bucket and a standby Hash bucket in the heavy part; searching fingerprints of data flow items in all logic units of the main hash bucket and the standby hash bucket; if the data flow items in the logic unit are found, reordering the data flow items in the logic unit according to the updated count value so as to keep descending order arrangement; if no idle logic unit exists in the main hash bucket and the standby hash bucket, executing the following conflict processing flow: selecting a data flow item with the minimum count value from the logic unit of the main hash bucket or the standby hash bucket as an expelling candidate, if the count value of the data flow item exceeds a preset threshold value, triggering a cross-bucket competition mechanism, executing cross-bucket competition, and if the count value of the data flow item exceeds the preset threshold value, executing the cross-bucket competition; and inserting the evicted data flow items into the light part for statistics.

The embodiment of the invention discloses a path planning method, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring position information and task information of a storage robot; according to the position information and the task information, utilizing a prediction model to output a prediction path set; determining a target function according to the prediction path set; and calculating according to the target function to obtain a planned path, and outputting the planned path to the storage robot. Therefore, after the position of the robot can be accurately obtained, path dynamic adjustment and operation look-ahead optimization can be realized through assistance of the prediction model. According to the method, the constructed target function is iteratively solved, path planning is achieved, local optimum can be avoided, the optimal sorting path can be quickly and accurately found, meanwhile, cooperation among work of multiple robots is guaranteed through a scientific scheduling strategy, the efficient and accurate sorting requirement of the manufacturing industry is met, the logistics cost is reduced, and the delivery speed is increased.

A method, computer program product, and computer system of artificial intelligence (AI) intervention to detect and mitigate an abnormality associated with an object moving during performance of a process. A trained recurrent neural network (RNN) determines, from sensor data, a probability (Pr1) that the abnormality existed at a first time, where Pr1 exceeds a threshold T1 and in response, an alternative generative adversarial network (AGAN) determines a probability (Pa1) that the abnormality existed at the first time. A score S1, which is computed as a function of Pr1 and Pa1, exceeds T1 and in response the RNN and the AGAN determines, from the senso data, a probability (Pr2) and a probability (Pa2), respectively, that the abnormality existed at a second time. A score S2, which is computed as a function of at least Pr2, Pa2, and (S1-T1), exceeds a threshold (T2) and in response, the abnormality is mitigated.

The application relates to the technical field of resource scheduling and discloses an intelligent resource scheduling supply method and system in a heterogeneous computing environment, which comprises the following steps: acquiring core heterogeneous data, performing exponential smoothing denoising to obtain a smooth state value, combining an HAT extended tuple, and analyzing to obtain a comprehensive capability score; automatically adjusting contribution degree by similarity of static characteristics and dynamic characteristics; constructing a coupling correction function, calculating a hybrid precision matching degree, combining an exponential product to reflect nonlinear correlation between targets; constructing a dynamic penalty function, optimizing a quantum genetic algorithm fitness function, and performing multi-target collaborative optimization; constructing a complexity coupling and dynamic adaptation mechanism to dynamically adjust a task segmentation ratio; designing a frequency and reconfiguration time index ratio to optimize FPGA bit stream preloading; constructing a synchronous delay coupling model to perform state synchronization optimization; balancing multidimensional feedback through a combined reward function; and improving explainability by quantifying the influence of decision factors through contribution degree entropy.

The application discloses a kind of based on script's satellite-borne distributed software task dynamic allocation method, it is characterized in that, include the following steps: S1. design based on script multi-task computing architecture, realize multi-task heterogeneous parallel computing;S2. based on script's software task is migrated between multiple computing nodes, and derive based on script's fine-grained backup strategy;S3. script dynamically binds C language computing library.In the application, by realizing script and script virtual machine technology, based on script, cross-node heterogeneous computing is realized under the basis that fixed script virtual machine and algorithm library are installed in each stage node;By script serialization technology, the transmission of script task between multiple virtual machines is realized, and heterogeneous node script dynamic deployment is realized;By modeling based on task directed acyclic graph of multi-task and multiple nodes, based on genetic algorithm, with the shortest completion time as objective function, the optimal strategy of multi-node task computing time is obtained.

The invention discloses a large language model artificial intelligence system which comprises a modeling processing unit of a large language model, and the modeling processing unit comprises an information extractor agent, a solver agent and a checker agent; the advantage value of each information extraction mode can be calculated through a composite reward function; through the composite reward function, consideration of format verification, constraint check, semantic analysis and text similarity can be realized for each information extraction mode; analyzing the description of the natural language problem based on the composite reward function, and obtaining a candidate solution by using the generated algebraic modeling language code; and finally, verifying the feasibility and format of the candidate solution obtained by the solver, and obtaining a problem solving result. According to the method, the large language model artificial intelligence system has strong expandability and generalization ability; and the modular design is easy to understand and improve, and effective knowledge acquisition and utilization can be realized.

The invention relates to the technical field of artificial intelligence, and discloses an interpretability AI-based statistical result dynamic visualization and diagnosis method, which comprises the following steps of: aiming at each data sample of a machine learning model, applying various interpretability algorithms in parallel, and generating and splicing a high-dimensional integrated interpretation vector; calculating the similarity between vectors through a multi-component interpretation kernel function, carrying out the dimension reduction of a high-dimensional vector set through a manifold learning algorithm, and constructing a low-dimensional interpretation manifold capable of reflecting the internal structure of the model decision logic; density clustering is carried out on the interpretation manifold, and an interpretation stability domain consistent with model decision logic and a logic mutation boundary with logic mutation are automatically recognized; the analysis result is visually presented, and an interactive diagnosis function is provided. According to the method, the global topology view of the model decision logic is constructed, so that the user can intuitively understand and diagnose the behavior pattern of the complex model, and the interpretability, transparency and reliability of the model are remarkably improved.

The invention relates to the technical field of resource scheduling, and discloses an intelligent resource scheduling supply method and system in a heterogeneous computing environment, and the method comprises the steps: obtaining core heterogeneous data, carrying out the exponential smoothing denoising, obtaining a smooth state value, combining an HAT extension tuple, and carrying out the analysis to obtain a comprehensive capability score; the contribution degree is automatically adjusted through the similarity between the static features and the dynamic features; constructing a coupling correction function, calculating a mixing precision matching degree, and reflecting nonlinear correlation between targets in combination with an exponential product; a dynamic penalty function is constructed, a quantum genetic algorithm fitness function is optimized, and multi-objective collaborative optimization is carried out; constructing a complexity coupling and dynamic adaptation mechanism, and dynamically adjusting a task segmentation proportion; optimizing FPGA bit stream preloading according to the index ratio of the design frequency to the reconfiguration time; constructing a synchronous delay coupling model, and performing state synchronization optimization; balancing multi-dimensional feedback through a combined reward function; the influence of decision factors is quantified through contribution degree entropy, and interpretability is improved.

A computing device, a procedure, and a program for calculating a correlation function from a structural model are provided.The computing device 100 for calculating a correlation function from a structural model comprises a structural model acquisition unit 110, which is configured to acquire a structural model comprising several types of atoms in a space, an atom type specification unit 120, which is configured to specify a particular type of atom in the structural model, and a correlation function calculation unit 130, which is configured to calculate a correlation function that is the ratio of a first radial distribution function to a second radial distribution function, wherein the first radial distribution function is a radial distribution function between atoms of the particular type of atom and wherein the second radial distribution function is a radial distribution function between atoms of the particular type of atom and atoms of two or more types of atoms including the particular type of atom.

The invention discloses an infinite storage database system and method based on function calculation mapping, and belongs to the technical field of database storage. The core of the system is that a physical storage demand is converted into a calculation demand through a unique mapping relation from a data value to a composite function, and infinite logic storage in a fixed physical space is realized. The system comprises a data mapping layer, a function calculation layer and a physical storage layer. When data is written, a data value is mapped to a unique composite function through a deterministic algorithm, and only an identifier of the function instead of original data is stored; and during data query, performing reverse calculation through the function identifier to obtain an original data value. The composite function is a predefined mathematical transformation function, and the one-to-one correspondence relation between the input data value and the output function identifier is ensured. According to the invention, through an innovative architecture of replacing computing power with storage, the limitation of physical storage space is broken through, and the infinite data storage capability is realized under the limited storage capacity.

A model learning facility (1) comprises a fixed branch selection unit (16) that selects fixed branches as branches to be excluded from learning objects from a plurality of branches contained in a learning model; a computation graph modification unit (13) that modifies a computation graph to be used into one derived from a first computation graph utilizing the plurality of branches and a second computation graph utilizing learning object branches obtained by excluding the fixed branches from the plurality of branches; an inter-branch distance computation unit (11) that computes distances between branches, including distances between features generated by each of the plurality of branches, in a state where the computation graph to be used has been modified into the first computation graph; and a loss function computation unit (12).which calculates a total sum of losses based on a predetermined loss function and the distances between branches, a branch update unit (14) that updates weight parameters in the learning object branches based on the total sum of losses in a state in which the computation graph to be used has been modified to the second computation graph.

The invention provides an exponential function calculation method, circuit and device and electronic equipment, and the method comprises the steps: receiving an exponential function calculation instruction, analyzing a first operand from the exponential function calculation instruction, and enabling the first operand to be an input value of a to-be-calculated exponential function; performing a * y + (b-c) operation by the floating point multiply-add unit, where a is a parameter related to the base of the exponential function; y is the first operand; b is a floating point number representation corresponding to the index offset value of the floating point data format; c is a parameter for indicating an approximate error; and performing bit extraction and combination on an output result of the floating point multiplication and addition unit through a normalization shift unit according to an exponential bit width and a mantissa bit width of a floating point data format to generate a floating point number conforming to the floating point data format, and outputting the floating point number as an exponential function approximate value of the input value y.

To provide an information processing method executed by an information processing system for formulating an optimization problem as a linear programming problem, and to provide the information processing system and a program.SOLUTION: The information processing method generates a second linear programming problem different from a first linear programming problem based on the first linear programming problem that is a detection target of a constraint condition violation. The first linear programming problem includes n first constraints x ≥ 1, 3x + 2y ≥ 6, - 3x - 2y ≥ 6, and first target functions x + y. The second linear programming problem includes n second constraints x ≥ 1 - u1, 3x + 2y ≥ 6 - u2, - 3x - 2y ≥ 6 - u3 (where ui> 0), and second target functions. The second constraint condition is obtained by relaxing the first constraint condition by ui, and the second objective function calculates a solution of the second linear programming problem and outputs information on the first constraint condition corresponding to ui that does not become zero.SELECTED DRAWING: None

The invention relates to a database operation method and device for function calculation, computer equipment, a computer readable storage medium and a computer program product. The method applied to any function computing node in a function computing cluster comprises the steps that a computing layer in a current function computing node receives a function computing request, wherein the function computing request comprises an object identifier and a function identifier; if the data of the object identifier is stored in the storage layer of the other function computing node, the computing layer forwards the function computing request to the other function computing node; if the data is stored in the storage layer of the current function calculation node, the calculation layer starts the database transaction, the function instance is executed based on the data of the object identifier, and when the function is executed, reading operation is performed on the distributed storage database provided by the storage layer through the database transaction, and data required by writing operation on the distributed storage database is cached; and after the function execution is finished, the database transaction is closed, the cached data is submitted to a storage layer for write operation, and the communication overhead is reduced.